1. Field of the Invention
The present invention relates to a unit and a method for determining gains of a PID (Proportion, Integral, Differential) controller using a genetic algorithm. More particularly, the present invention relates to a unit and a method for determining gains of the PID controller using a genetic algorithm wherein control gains, i.e., PID gains to control an object system, are determined by means of a random search.
2. Description of the Prior Art
Generally, a PID controller is a controller of a type which has been used most widely in an industrial place. Since this PID controller is very sensitive to the object system, however, there is no exact theory or rule of determining proper PID gains for every object system. Further, even though a number of theories exist, it is possible to determine PID gains only when a mathematical model with respect to the object system exists. Therefore, unless the exact mathematical model exists, it is very difficult to determine the proper PID gains. Further, to determine the gains is very troublesome work which is accomplished by skilled specialists. This is accomplished through many trials and errors by the skilled specialists and many experiences for a long period of time. In PID control, determination of parameters for proportional gain, integral time, and differential time to improve a characteristic of the system is most important.
The PID control is to control simultaneously the proportional control mode, integral control mode, and differential control mode so that the system reaches a target value in a stable state within a fast period of time as soon as possible, when a difference between a reference input and an output value of the object system, i.e., a derivative, is input to an input element of the controller. In PID control, increase of the derivative is prevented in the integral control mode, and decrease of a remaining derivative is effected in the differential control mode. To increase a value of proportional gain enables the system to reach the target value within a fast period of time, but the system is unstable by producing a large overshot. To decrease a value of the proportional gain, on the other hand, enables a normal derivative to occur often, and thus, the need to automatically tune the PID control is rising.
One technique of automatically determining gain of the PID controller is disclosed in U.S. Pat. No. 5,295,061, entitled "Control parameter tuning unit and a method of tuning parameters for a control unit". This technique uses a fuzzy logic instead of a neural logic, thus being very expensive.
Further, another conventional technique is disclosed in "EXACT" by Foxbor company, 1975, available on the market. In this technique, after obtaining the model of the object system by monitoring the output of the object system, proper gain is obtained by mathematical computation from the obtained model, so that a response characteristic is very slow because of the emphasis on robustness with respect to disturbances.
The above technique of automatically determining the gain of the PID controller disclosed in U.S. Pat. No. 5,295,061 is now described with reference to FIG. 1.
Referring to FIG. 1, an automatic gain determining unit 5 includes a reference setting value inputting unit 10 for inputting reference data to control an output state of an object system 40, a fuzzy control unit 30 for comparing the reference data input through the reference setting value inputting unit 10 with output data of the object system 40, determining whether the output data of the object system 40 satisfies a user's setting value, and generating control signals to control PID gains if there is no satisfaction, and a PID controller 20 for converting the PID gains by the control signals output from the fuzzy control unit 30 to control the object system 40 by a control signal corresponding to the converted gains.
The fuzzy control unit 30 includes a fuzzy control knowledge base 30a storing fuzzy control rules and membership functions, and a fuzzy inference unit 30b effecting inference based on the fuzzy rules and membership functions stored in the fuzzy control knowledge base 30a.
An assumption is made that gain of the PID controller is set through a simple mathematical model of the object system 40. The reference setting value is input through the reference setting value inputting unit 10 so that the object system 40 is placed in any state. Then, the setting value is supplied to the fuzzy control unit 30 and the PID controller 20. If the PID controller 20 supplies the control signals to the object system 40, the object system 40 makes a control action in accordance with the input control signal to produce a changed output.
If a temperature is controlled, the reference input setting value comes to a temperature and the output of the object system 40 comes to a temperature.
The output is fed back to the fuzzy control unit 30 and the PID controller 20. Then, the fuzzy control unit 30 compares the reference setting value with the output of the object system 40 to determine whether the output of the object system 40 is satisfied with the user's setting value. If there is no satisfaction, after bringing out a necessary knowledge in the fuzzy control knowledge base 30a, and determining that any gain has to be changed to any extent through the fuzzy inference unit 30b, the gain of the PID controller 20 is changed.
Thus, the PID controller 20 operates to control the object system 40 with the changed gain.
Therefore, the fuzzy inference unit 30b requires some computing times. As a result of the fact that it takes some time for the fuzzy inference unit 30b to define a proper inference, a response velocity is lowered and, therefore, the automatic gain determining unit 5 can only be used in chemical reaction equipment or a water level controlling mechanism, etc., which does not require a high response velocity. Further, this unit requires the fuzzy control knowledge base 30a necessary for the fuzzy inference unit 30b and memories (DRAM, SRAM, or flash memory) storing the membership functions. Since the fuzzy inference unit 30b requires computing, a microprocessor having a large capacity is indispensable, thus raising a problem in that an inexpensive PID controller can not be manufactured.